Internet companies are constructing large-scale AI clusters with commodity Ethernet switches for AI model training to support their businesses. AI training workloads impose stringent network requirements, mandating that cluster networks deliver high peak throughput while maintaining robustness and resilience in the face of link failures. We present SGLB, a distributed, global congestion-aware load balancing system for AI clusters. SGLB operates a control-plane protocol, SyncMesh, to enable a new load balancing abstraction in modern commodity switches—Global Load Balancing (GLB) engine—which utilizes global congestion information to distribute traffic across all available paths. We address three key challenges in designing SGLB: fast routing convergence to minimize downtime in the event of link failures, scalable maintenance of congestion profiles within the constraints of limited switch hardware resources, and preventing GLB throughput suppression in scenarios where path bandwidths are asymmetric. We prototype SGLB and conduct extensive experiments to evaluate SGLB. SGLB ensures rapid routing convergence in the event of link failures, recovering in as little as 45 μs to guarantee network robustness for long-term, stable model training. Additionally, SGLB effectively load-balances traffic across paths, avoiding those with global congestion, which accelerates All-to-All collective communication by up to 60%.

Accounting the problem of small sample size in tomato yield statistics, a dual-fusion prediction analysis model based on Bayes theory and deep learning algorithm Cascade-PSPNET is proposed. The tomato yield prediction is conducted based on remote sensing image time series analysis and multi-source information fusion theory with Bayesian theory and credibility weighting. First, the area of tomato planting is analyzed through semantic segmentation algorithms of remote sensing images during the tomato planting process. The yield is predicted by variables such as planting area fluctuation, disaster cycle fluctuation, etc. Through analyzing the reduced yield affected by disasters in different time periods of remote sensing images during planting process, the value chain of tomato industry is calculated by comprehensively analyzing price-value system, inflation coefficient, and unit area yield. At the same time, the annual patent application volume is used to predict the change of tomato yield year by year according to Bayesian theory, and the relationship between annual patent application volume and tomato yield year by year under different confidence levels is analyzed. The results show that it is feasible to use the Bayes method with semantic segmentation algorithms of remote sensing images to predict tomato yield. Next, the experiment of fusion prediction between two prediction models in the same target area will be carried out for verification.